While 82-percent model accuracy might be in the ballpark of what is achievable for this task, our model does present some imbalances that, while common to this type of mapping, should be flagged for caution, especially in the context of the objective of impact evaluation. The dilemma of imbalanced errors, which occurs when one outcome (burning, in this case) occurs more frequently than the other (not burning), is common in such tasks [1]}, [2]} and was a topic of careful consideration in this work.In our case, burning is observed more frequently than not burning; in most fire mapping cases, burning is usually considered the rare event. Our approach does not solve the issue but rather provides bounds within which the solution likely lies. Moving the classification threshold provides a choice regarding how we should balance the sensitivity, specificity, and total accuracy of our classification output. Other options to address imbalanced collection of burn and no-burn labels include methods such as Balanced Random Forest, which uses a cost function or weighting of classes to compensate for the underrepresented unburned class [3]}. In our case, the elaborate verification scheme for the randomized control led trial resulted in enough verified unburned observations that class balance is satisfactory, albeit not ideal.
